For instance, the entropy minimization method [1]} chooses the entropy of the output as the loss to encourage the model to produce a prediction with low uncertainty. This is great in the case of curated data as we know that each unlabeled image corresponds to one of the known categories. However, in the case of un-curated data, the model may be uncertain for data from other unknown categories. Thus, minimizing the entropy loss may not be a good idea. We support this hypothesis in our experiments by adding unknown categories from the target domain to the standard unlabeled data.
